No Arabic abstract
The CASCADE3 Monte Carlo event generator based on Transverse Momentum Dependent (TMD) parton densities is described. Hard processes which are generated in collinear factorization with LO multileg or NLO parton level generators are extended by adding transverse momenta to the initial partons according to TMD densities and applying dedicated TMD parton showers and hadronization. Processes with off-shell kinematics within $k_t$-factorization, either internally implemented or from external packages via LHE files, can be processed for parton showering and hadronization. The initial state parton shower is tied to the TMD parton distribution, with all parameters fixed by the TMD distribution.
We discuss prospects for Monte Carlo event generators incorporating the dynamics of transverse momentum dependent (TMD) parton distribution functions. We illustrate TMD evolution in the parton branching formalism, and present Monte Carlo applications of the method.
We present a new strategy using artificial intelligence (AI) to build the first AI-based Monte Carlo event generator (MCEG) capable of faithfully generating final state particle phase space in lepton-hadron scattering. We show a blueprint for integrating machine learning strategies with calibrated detector simulations to build a vertex-level, AI-based MCEG, free of theoretical assumptions about femtometer scale physics. As the first steps towards this goal, we present a case study for inclusive electron-proton scattering using synthetic data from the PYTHIA MCEG for testing and validation purposes. Our quantitative results validate our proof of concept and demonstrate the predictive power of the trained models. The work suggests new venues for data preservation to enable future QCD studies of hadrons structure, and the developed technology can boost the science output of physics programs at facilities such as Jefferson Lab and the future Electron-Ion Collider.
We review the main software and computing challenges for the Monte Carlo physics event generators used by the LHC experiments, in view of the High-Luminosity LHC (HL-LHC) physics programme. This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group as an input to the LHCC review of HL-LHC computing, which has started in May 2020.
PHANTOM is a tree level Monte Carlo for six parton final states at proton--proton, proton--antiproton and electron--positron collider at O(alpha_ew^6) and O(alpha_ew^4*alpha_s^2) including possible interferences between the two sets of diagrams. This comprehends all purely electroweak contribution as well as all contributions with one virtual or two external gluons. It can generate unweighted events for any set of processes and it is interfaced to parton shower and hadronization packages via the last Les Houches Accord protocol. It can be used to analyze the physics of boson boson scattering, Higgs boson production in boson boson fusion, t-tbar and three boson production.
The Monte Carlo program {tt WWGENPV}, designed for computing distributions and generating events for four-fermion production in $e^+ e^- $ collisions, is described. The new version, 2.0, includes the full set of the electroweak (EW) tree-level matrix elements for double- and single-$W$ production, initial- and final-state photonic radiation including $p_T / p_L$ effects in the Structure Function formalism, all the relevant non-QED corrections (Coulomb correction, naive QCD, leading EW corrections). An hadronisation interface to {tt JETSET} is also provided. The program can be used in a three-fold way: as a Monte Carlo integrator for weighted events, providing predictions for several observables relevant for $W$ physics; as an adaptive integrator, giving predictions for cross sections, energy and invariant mass losses with high numerical precision; as an event generator for unweighted events, both at partonic and hadronic level. In all the branches, the code can provide accurate and fast results.